Abstract 4872: Gene fusion detection in pediatric tumor samples utilizing multi-caller fusion detection approach and integrative data analysis

Abstract

Gene fusions are an important mechanism of oncogenesis in pediatric cancers. Fusion detection from RNA sequencing data is challenging for several reasons including a high false positive rate by individual fusion callers. We developed a new pipeline for gene fusion detection utilizing a multi-caller fusion detection approach and focusing on improved specificity, annotation and visual presentation.

Multiple fusion detection algorithms, integrative data analysis and known fusions and cancer genes annotation databases are integral parts of our new pipeline. The following fusion callers were included in the pipeline: Chimerascan, TopHat Fusion and STAR-Fusion. Fusions called by at least two fusion callers were included in the final results and annotated by utilizing the TARGET database (Tumor Alterations Relevant for Genomics-driven Therapy) and TICdb (Translocation breakpoints In Cancer database). A Venn diagram was produced for visual presentation of candidate fusions detected by multiple fusion callers. IGV (Integrative Genomics Viewer) was used to visualize the alignment of reads at fusion break points.

The fusion detection pipeline was tested with RNA sequencing data from 11 pediatric tumor samples either suspected to harbor fusions based on diagnosis (n=7; renal cell carcinoma, osteosarcoma, synovial sarcoma, glomus tumor, undifferentiated sarcoma) or known to have fusions based on standard methods (n=4; Ewing-like sarcoma, EWSR1 FISH+ sarcoma, clear cell sarcoma). In most cases, fusions previously known to be present were identified with our pipeline. For the difficult to detect fusion CIC-DUX4, optimization by adding another fusion caller (FusionCatcher) and adjusting the filtering and annotation parameters was required to increase sensitivity. Novel fusions were identified in cases suspected to harbor fusions and in some cases these novel fusions have been validated to be present with other methods. In one case (synovial sarcoma) the expected fusion, which was not detected with standard testing (FISH), was identified with our pipeline. The pipeline was also successfully utilized to analyze prostate cancer samples (PMID: 27167109).

Our new multi-caller fusion detection pipeline has been successful in increasing specificity and decreasing the false positive rate for gene fusion calling in transcriptomic data, while being sensitive enough to detect the more challenging gene fusions. Additional updates to the pipeline are anticipated like the realignment against a modified reference sequence including the gene rearrangement for improved sensitivity.

Citation Format: Alma Imamovic, Marian Harris, Alanna Church, Brian Crompton, Eliezer Van Allen, Katherine Janeway. Gene fusion detection in pediatric tumor samples utilizing multi-caller fusion detection approach and integrative data analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4872. doi:10.1158/1538-7445.AM2017-4872